Comparison of Blood Pressure Medications on Metabolism: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Comparison of Blood Pressure Medications on Metabolism (NCT00642434), a randomized phase 4 study. The primary outcome is Number of Participants Assessed for Change in Markers of Inflammation (PAI-1) Following Treatment Compared to Baseline.

2. Clinical Question

3. Trial Design

The registry describes a completed Phase 4 trial with randomized allocation, triple masking, and a parallel-group model. The interventions were carvedilol and metoprolol. The recorded enrollment is 22, and the registry lists University of New Mexico as the sponsor. The listed start date is 2004-01 and the primary completion date is 2008-06. The record does not explain the meaning or implementation of triple masking, identify which roles were masked, or describe how randomization was generated or concealed.

The brief summary says participants were to receive one of the two beta-blockers and be followed for 5 months. It describes four inpatient studies for each subject, including an oral glucose tolerance test, measurements of inflammatory and oxidative stress markers, and assessment of endothelial function by brachial artery ultrasound and laser skin Doppler. These details describe the stated study plan; the registry record does not report whether every participant completed each planned assessment.

The primary outcome is framed as the number of participants with a change in PAI-1 markers from baseline following treatment. The outcome timeframe is 5 months. The outcome records list 9 participants in the metoprolol group and 11 in the carvedilol group, with a value of 0 in both. These recorded group sizes do not match the overall enrollment figure, and the registry record does not explain the difference. The safety records use a different at-risk count for each listed group; the reason for that difference from the outcome records is not reported.

No analysis results are posted in the registry record. In particular, there is no reported treatment-effect estimate, confidence interval, or statistical test for the primary outcome. The record also does not report a detailed statistical analysis plan, a sample-size justification, or interim analyses. The status is listed as completed, but that status alone does not supply the missing analysis details.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Number of Participants Assessed for Change in Markers of Inflammation (PAI-1) Following Treatment Compared to Baseline, measured over 5 months.

The primary endpoint is described as the number of participants whose PAI-1 marker changed following treatment compared with baseline, assessed over 5 months. PAI-1 is identified in the outcome title as a marker of inflammation. The endpoint is represented as a binary count: participants are counted according to whether a change was recorded, rather than the amount by which the marker changed.

As a general statistical point, a binary endpoint can be useful when the research question concerns how many participants meet a stated event definition. It does not, by itself, show the size of a biomarker change or distinguish small changes from large ones. The registry record does not define what magnitude or direction qualified as a change, report the underlying PAI-1 measurements, or explain how the event was determined. It also does not give a more detailed endpoint population.

The posted outcome value is 0 for each group. That is the recorded event count, not an estimate of the difference in PAI-1 levels between treatments. The registry record does not report a confidence interval or statistical test for this endpoint, so the precision and comparative uncertainty cannot be assessed from the posted outcome alone.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry identifies a randomized, triple-masked, parallel-group study. These design labels describe intended features of how the trial was organized, but the registry record does not provide operational detail. It does not report how the random sequence was generated, how assignments were concealed, or who was masked. The labels alone cannot resolve those questions.

What does randomization contribute?

In general, random assignment is intended to make treatment groups comparable with respect to measured and unmeasured characteristics, on average across repeated trials. It does not guarantee that groups in a particular trial will have identical baseline characteristics. Assessing comparability ordinarily requires baseline information, which is not reported in the registry record for this outcome. Randomization also does not remove the need to describe exclusions, adherence, missing measurements, and the analysis population.

What does the parallel-group label mean?

In a parallel-group design, participants are assigned to a treatment group and outcomes are assessed within that group over follow-up. The registry identifies carvedilol and metoprolol as the study drugs and describes follow-up over 5 months. It does not report whether participants switched treatment, how adherence was measured, or whether all planned study visits occurred. Such information matters because deviations from assigned treatment can affect how a treatment comparison is interpreted.

How should the binary outcome be read?

The PAI-1 endpoint is recorded as a participant count, with the event described as a change following treatment compared with baseline. In general, a binary endpoint reduces each participant's result to an event or no-event category. Its interpretation depends on a clear event definition, including how much change qualifies and how direction is handled. The registry record does not report those criteria or the underlying continuous measurements. Thus, the count does not reveal the size or direction of biomarker changes.

For an unadjusted comparison of binary outcomes, statistical methods use both the event counts and the number of participants represented in each group. When event counts are zero in both groups, ordinary estimates may be unavailable or require methods designed for sparse outcomes. The record explicitly says that values are missing or not estimable for a two-arm binary comparison, and it posts no analyses. No recalculated comparative result is therefore available in the registry record. A method should not be inferred from the trial's design labels or from the outcome values alone.

What would uncertainty measures add?

In general, an effect estimate would describe the direction and magnitude of a group contrast under a specified analysis. A confidence interval would convey uncertainty around that estimate, conditional on the method and assumptions. A test result could address compatibility with a null hypothesis, but would not replace the effect estimate or show whether an effect is clinically important. None of these inferential results is reported for the primary outcome.

For binary outcomes with sparse counts, uncertainty can be substantial, and some standard calculations may not produce stable or finite estimates. Exact methods or other approaches may sometimes be considered, but the appropriate choice depends on the analysis plan and estimand. The registry record does not report such a plan or any method used for this endpoint. It would therefore be inappropriate to select a method after seeing the posted counts and present its output as the trial's own analysis.

What is the role of planning calculations?

General sample-size illustrations use assumptions about the target effect, significance level, allocation, and statistical method to show how a future study might be planned. They are planning aids, not observed trial results and not evidence that a completed study had adequate power. The stated planning assumptions describe an illustration based on the observed effect, with a two-sided significance level of 0.05, equal allocation, and a normal approximation. The record says the effect to detect is a stated fraction of the observed effect. It does not report the trial's own sample-size justification. No numeric planning target is included in the registry entries described for this analysis, and no target should be inferred.

As a general matter, a study's ability to estimate an effect depends on the number of outcome events and the analysis method, not only on enrollment. A zero event count in each group provides especially limited information for distinguishing possible group effects. Planning illustrations cannot repair missing outcome definitions, missing analyses, or unknown outcome availability. They also should not be described as observed power.

The record reports a binary outcome, no posted analysis, and no inferential result. Its design labels and raw outcome entries are not enough to reconstruct a prespecified analysis. The statistical interpretation must therefore remain descriptive and confined to what the registry records.

10. Exact Estimates for Each Arm

No two-arm comparison is shown for this trial, but each arm's result can still be summarized with exact interval estimates computed from the posted counts. The Clopper-Pearson interval is exact and conservative; the Wilson interval is a common closer-to-nominal alternative; the Bayesian credible interval uses a uniform Beta(1, 1) prior. For small groups the three can differ noticeably.

R code and output

arm    <- c("Metoprolol", "Carvedilol")
n      <- c(9, 11)
events <- c(0, 0)
for (i in seq_along(n)) {
  ci <- binom.test(events[i], n[i])$conf.int          # Clopper-Pearson exact interval
  cr <- qbeta(c(0.025, 0.975), 1 + events[i], 1 + n[i] - events[i])   # Bayesian, uniform Beta(1,1) prior
  wi <- (function(x, n) { p <- x / n; z <- qnorm(0.975); d <- 1 + z^2 / n
    c((p + z^2 / (2 * n) - z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d, (p + z^2 / (2 * n) + z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d) })(events[i], n[i])
  cat(sprintf("ARM|%s|%d|%d|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f\n", arm[i], n[i], events[i], 100 * events[i] / n[i],
              100 * ci[1], 100 * ci[2], 100 * wi[1], 100 * wi[2], 100 * cr[1], 100 * cr[2]))
}

ARM  Metoprolol  9  0  0.0  0.0  33.6  -0.0  29.9  0.3  30.8
ARM  Carvedilol  11  0  0.0  0.0  28.5  -0.0  25.9  0.2  26.5

11. Multiplicity, Missing Data and Analysis Populations

The registry lists one outcome measure and no secondary outcomes as posted. It records 0 analyses. This does not establish that the investigators collected no other measures: the brief summary describes blood pressure, blood sugar control, endothelial function, inflammation, oxidative stress, and coagulation. Rather, those measures are not presented as posted secondary results in the registry record.

Multiplicity arises when researchers make several statistical comparisons or test several outcomes. In general, considering many tests can increase the chance of a seemingly notable result arising by chance, so an analysis plan may specify which outcomes are primary and how multiple comparisons will be handled. The registry record does not report a multiplicity adjustment, a hierarchy of tests, or a statistical analysis plan. With no analyses posted, the handling of multiple comparisons cannot be assessed.

Missing-data details are also not reported in the registry record. It does not state how many participants had PAI-1 measurements at baseline or follow-up, whether any values were missing, how missing measurements were handled, or why outcome-group counts differ from overall enrollment. It does not report whether the primary analysis included everyone as randomized, only participants with available measurements, or another analysis population.

These omissions matter because the interpretation of a count depends on who was assessed and how unavailable observations were treated. No conclusion about missingness, exclusions, adherence, or the analysis population can be drawn from the posted outcome counts alone.

12. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

The safety record lists 10 participants at risk for serious adverse events in each treatment group, with 0 serious adverse events recorded for metoprolol and 0 for carvedilol. It also lists 10 at risk for other adverse events in each group, with 0 other adverse events recorded in either group. Deaths are recorded as 0 in both groups. No top serious adverse-event terms are listed.

These are registry counts, not estimates of comparative safety. A zero recorded count means that the record reports no events in that category for the listed at-risk group. It does not establish that an event could not occur, and it does not quantify how precisely the true risk is known. As a general statistical point, uncommon events can remain uncertain when few participants are observed.

The safety at-risk counts differ from the primary-outcome group counts, and the registry record does not explain why. It does not report event definitions, collection procedures, follow-up completeness, or whether all adverse events were actively solicited. The figures should therefore be read as the safety data entered in the registry, not as a complete account of every possible adverse effect.

13. What the Result Does, and Does Not, Mean

The posted primary outcome records zero participants with a change in PAI-1 markers in each treatment group. That is a descriptive registry result for the stated binary endpoint. It does not report the magnitude of PAI-1 measurements, the direction of any individual changes, or a statistical contrast between carvedilol and metoprolol.

The registry does not report a treatment-effect estimate or uncertainty interval, and it lists no analyses. It therefore does not establish that the treatments had the same effect, that either treatment altered PAI-1, or that one treatment was superior. A lack of a reported comparison is not evidence of equivalence or of no effect.

The study summary names several other areas of interest, including blood pressure, blood sugar control, endothelial function, inflammation, oxidative stress, and coagulation. The registry entries do not provide results for those outcomes. The safety record reports zero serious adverse events, zero other adverse events, and zero deaths in the recorded groups, but these counts do not establish the absence of risk or provide a precise comparison of safety.

As a general statistical principle, a result is most informative when its outcome definition, analysis population, effect estimate, and uncertainty are all clear. Here, the event definition, missing-data handling, and population used for analysis are not reported in the registry record. The conclusion must stay narrow: the posted entries contain zero recorded PAI-1 change events in each group, with no inferential analysis available to interpret a treatment contrast.

14. Why This Trial Matters Statistically

This trial is statistically instructive because it illustrates the difference between a recorded outcome count and an analyzed treatment effect. The registry contains a binary primary outcome with a value of 0 in each group, but it does not provide an estimate or test comparing those groups. A reader cannot infer the precision of a comparison from the counts alone.

The record also distinguishes several kinds of information that should not be conflated. Overall enrollment is recorded as 22, primary-outcome groups have their own listed counts, and safety groups have at-risk counts. The record does not explain the differences among those counts. Recognizing that mismatch is important: denominators have meaning only when the population and data source are clear.

More generally, a biomarker endpoint defined as a participant-level event loses information about the amount and direction of change. A transparent event definition and the underlying measurement strategy help readers understand what the count represents. Those details are not reported for the PAI-1 endpoint.

The case also shows why a completed status is not a substitute for posted results. The registry identifies the design and summarizes planned assessments, yet lists no analyses. Statistical interpretation must follow the results actually recorded, not assume that a detailed analysis exists simply because the trial was completed. The posted data support a limited descriptive statement, while key information needed for comparative inference remains unavailable.

15. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several limitations are specific to the registry information for this trial. The primary outcome description says the endpoint counts participants with a change in PAI-1 compared with baseline, but the registry record does not define the threshold, direction, assay, or measurement procedure used to determine a qualifying change. It also does not provide actual PAI-1 values, so the magnitude and pattern of biomarker change cannot be assessed.

The outcome entries list 9 participants for metoprolol and 11 for carvedilol, while overall enrollment is listed as 22. Safety entries list 10 at risk in each treatment group. The record does not explain these differing counts, identify the analysis population, or say whether the counts reflect missing observations, exclusions, or another reporting convention. No explanation should be assumed.

The registry posts no analyses and states that a two-arm comparison for the binary endpoint is missing or not estimable. There is no effect estimate, confidence interval, or test result. Statistical methods, handling of missing data, treatment adherence, baseline characteristics, and the rationale for the study's size are not reported in the registry record. The masking and randomization labels are not accompanied by implementation details.

The brief summary describes several planned areas of assessment, but no results for those additional outcomes are included in the registry entries. The safety counts are also limited to the categories recorded and do not provide detailed event definitions or follow-up information. These limitations restrict interpretation to the registry's descriptive entries and prevent a broader assessment of efficacy or safety.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Comparison of Blood Pressure Medications on Metabolism — NCT00642434.
• Registry record last updated 2023-12-26; status last verified 2023-12.
• Results first posted 2023-12-26.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

19. Record Summary

The record describes a completed, randomized, triple-masked, parallel-group Phase 4 study sponsored by University of New Mexico. It compares carvedilol with metoprolol in patients with type 2 diabetes and hypertension and lists enrollment of 22. The primary outcome is the number of participants with a change in PAI-1 markers from baseline over 5 months.

The posted outcome entries record 0 for each group, with group counts of 9 for metoprolol and 11 for carvedilol. No analysis is posted, and the registry indicates that a binary two-arm comparison is not estimable from the available values. Safety entries report 0 serious adverse events, 0 other adverse events, and 0 deaths in each group among the listed at-risk participants. The registry record does not explain the different outcome and safety counts or provide the analysis details needed for inferential conclusions.